The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1269–1273, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1269-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1269–1273, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1269-2019

  05 Jun 2019

05 Jun 2019

DETECTION OF SHALLOW WATER AREA WITH MACHINE LEARNING ALGORITHMS

N. Yagmur1, N. Musaoglu1, and G. Taskin2 N. Yagmur et al.
  • 1ITU, Civil Engineering Faculty, Department of Geomatics Engineering 34469 Maslak Istanbul, Turkey
  • 2ITU, Institute of Earthquake Engineering and Disaster Management, 34469 Maslak Istanbul, Turkey

Keywords: Shallow water, Remote sensing, Machine learning, Water indices, SVM, Feature selection

Abstract. Remote sensing techniques has been widely used for detecting water bodies in especially wetlands. Different classification methods and water indices has used for this purpose and there are numerous studies for detecting water bodies. However, detecting shallow water area is difficult comparing with deep water bodies because of the mixed pixels. Akgol Wetland is chosen as study area to detect shallow water. For this purpose, Sentinel 2 satellite image, which gives more accurate results thanks to higher spatial resolution than the images having medium spatial resolution, is used. In this study, two classification approaches were applied on Sentinel 2 image to detect shallow water area. In the first approach, effectiveness of indices was determined and classification of spectral bands with indices shows higher accuracy than classification of only spectral bands by using support vector machine classification method. In the second approach, support vector machine recursive feature elimination method used for the most effective features in the first approach. Besides overall accuracy of only spectral bands is obtained as 88.10%, spectral bands and indices’ accuracy was obtained as 91.84%.